Thermo Fisher Scientific (TMO) Selling, General & Administrative (2009 - 2026)
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Analysis
Thermo Fisher Scientific (TMO) Selling, General & Administrative (2009 - 2026) Analysis & Trends
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | SG&A (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 257.11 Bn | 233.62 Bn | 5.13 Bn | 2.32 Bn |
| 2 | Abbott Laboratories | 191.51 Bn | 165.37 Bn | 7.39 Bn | 4.27 Bn |
| 3 | Danaher | 161.07 Bn | 140.73 Bn | 3.84 Bn | 2.16 Bn |
| 4 | Intuitive Surgical | 159.13 Bn | 139.42 Bn | 2.08 Bn | 633.40 Mn |
| 5 | Medtronic | 131.12 Bn | 96.37 Bn | 6.34 Bn | 3.20 Bn |
| 6 | Stryker | 124.59 Bn | 110.78 Bn | 4.25 Bn | 2.31 Bn |
| 7 | Boston Scientific | 62.28 Bn | 57.71 Bn | 3.43 Bn | 1.68 Bn |
| 8 | Becton Dickinson | 58.65 Bn | 55.80 Bn | 2.32 Bn | 1.26 Bn |
| 9 | Edwards Lifesciences | 58.48 Bn | 41.68 Bn | 1.35 Bn | 560.90 Mn |
| 10 | Agilent Technologies | 49.37 Bn | 42.26 Bn | 1.04 Bn | 475.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 26, 2026 | 2.32 Bn |
| Jun 27, 2026 | 2.33 Bn |
| Mar 28, 2026 | 2.18 Bn |
| Dec 31, 2025 | 2.35 Bn |
| Sep 27, 2025 | 2.16 Bn |
| Jun 28, 2025 | 2.14 Bn |
| Mar 29, 2025 | 2.08 Bn |
| Dec 31, 2024 | 2.20 Bn |
| Sep 28, 2024 | 2.10 Bn |
| Jun 29, 2024 | 2.11 Bn |
| Mar 30, 2024 | 2.18 Bn |
| Dec 31, 2023 | 2.13 Bn |
| Sep 30, 2023 | 2.05 Bn |
| Jul 1, 2023 | 2.15 Bn |
| Apr 1, 2023 | 2.12 Bn |
| Dec 31, 2022 | 2.30 Bn |
| Oct 1, 2022 | 2.21 Bn |
| Jul 2, 2022 | 2.21 Bn |
| Apr 2, 2022 | 2.28 Bn |
| Dec 31, 2021 | 2.28 Bn |
API Access
Thermo Fisher Scientific Selling, General & Administrative API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=selling-general-and-administrative&ticker=TMO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "selling-general-and-administrative", "ticker": "TMO", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=selling-general-and-administrative&ticker=TMO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();